---
title: "AI Development: Hardware and Software You need"
description: "The development of AI libraries needs sophisticated hardware and software (top programming languages). Here's a list for your reference."
url: https://srinimf.com/2023/02/20/ai-development-hardware-and-software-you-need/
date: 2023-02-20
modified: 2026-10-10
author: "Srini"
image: https://i0.wp.com/srinimf.com/wp-content/uploads/2023/02/ai.jpg?fit=2240%2C1260&ssl=1
categories: ["Artificial Intelligence"]
type: post
lang: en
---

# AI Development: Hardware and Software You need

**N**ow, AI development projects have kickstarted, it’s time [to know](https://srinimf.com/2016/01/07/how-to-know-new-ispf-version-changes-with-sarea-command/) the hardware and software to develop artificial intelligence projects.

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In simple terms, AI (Artificial Intelligence) is an area of computer science [that a](https://srinimf.com/2016/01/28/what-mainframe-programmers-should-learn-next/)ims to make machines do intelligent things.

AI is widespread in healthcare, manufacturing, driverless cars, finance, and agriculture.

*Ai hardware software *

## AI Hardware requirement

**Standard Computers: **For a beginner, a current standard computer is sufficient, including an Intel or AMD CPU with a 2 GHz clock, 8 GB RAM, 500 GB hard drive, and a current Windows, Mac, or Linux operating system. 

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**Computers with GPUs:** For a more advanced user, if you want to train more data or build more complicated, larger AI models, you will need a more advanced computer, i.e., a computer with a graphics processing unit (GPU).

That typically includes a CPU with 8 cores, 32 GB RAM, 1 TB hard drive, and an NVIDIA GeForce RTX 1080 (or 2080) Series 8GB GPU. 

**Computers with FPGAs**: Field Programmable Gate Array (FPGA) uses in AI. Altera and Xilinx are the two most well-known FPGA manufacturers.

For example, Intel achieved 3,700 frames per second processing with its Arria 10 GX 1150 FPGA. An Intel Arria 10 GX FPGA can easily cost more than $5,000.

**Graphcore’s IPUs**: Graphcore unveiled its second-generation processor using a 7 nm process, which packs about 60 billion transistors on an 800-square-millimeter integrated circuit with 1,472 computing cores and 900 MB of local memory. [Here’s a link to the Graphcore machine.](https://www.graphcore.ai/)

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## AI Software tools

As you know, C and C++ are the top languages for developing AI libraries. The libraries that have written in these languages have fast processing speeds.

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The libraries that are written in **C and C++** are  OpenCV ([`https://opencv.org/`](https://opencv.org/)) for computer vision and YOLO ([`https://pjreddie.com/darknet/yolo/`](https://pjreddie.com/darknet/yolo/)) for object detection.

- Google TensorFlow ([`https://github.com/tensorflow/tensorflow`](https://github.com/tensorflow/tensorflow))
- Caffe ([`https://github.com/intel/caffe`](https://github.com/intel/caffe))
- Microsoft Cognitive Toolkit (CNTK) ([`https://docs.microsoft.com/en-us/cognitive-toolkit/`](https://docs.microsoft.com/en-us/cognitive-toolkit/))
- MLPACK Library ([`https://www.mlpack.org/`](https://www.mlpack.org/))
- SHARK Library ([`https://github.com/Shark-ML/Shark`](https://github.com/Shark-ML/Shark))
- OpenNN ([`https://www.opennn.net/`](https://www.opennn.net/))

**Java is another popular language**. All apps on Android phones are written in Java. To use Java in AI development, you can use the following libraries:

- Weka ([`https://www.cs.waikato.ac.nz/ml/weka/`](https://www.cs.waikato.ac.nz/ml/weka/))
- Deeplearning4j (`[https://deeplearning4j.org/](https://deeplearning4j.org/)`)

**C# is popula**r for developing Windows desktop graphical user interface (GUI) programs. With C#, you can use the following libraries:

- Emgu CV ([`http://www.emgu.com/wiki/index.php/Main_Page`](http://www.emgu.com/wiki/index.php/Main_Page))
- ML.NET ([`https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet`](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet)

**Python is an upcoming language **in the development of AI libraries. To use Python in AI development, you can use several libraries such as Numpy, Pandas, Matplotlib, and NLTK.

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You can also use several open-source frameworks:

- Scikit-Learn ([`https://scikit-learn.org/stable/`](https://scikit-learn.org/stable/))
- Keras ([`https://keras.io/`](https://keras.io/))
- Google TensorFlow ([`https://github.com/tensorflow/tensorflow`](https://github.com/tensorflow/tensorflow))
- Facebook’s PyTorch ([`https://pytorch.org/`](https://pytorch.org/))
- Caffe2 ([`https://caffe2.ai/`](https://caffe2.ai/))
- Baidu’s Paddle ([`https://github.com/PaddlePaddle/Paddle`](https://github.com/PaddlePaddle/Paddle))

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